{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c3d1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla pyreadr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d9fbbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e6dc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe4be2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from itertools import product\n",
    "    from fastcore.test import test_eq, test_fail, test_warns\n",
    "    from dotenv import load_dotenv  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0241d5c1-8d11-4cb9-9259-cda5fffa7ea8",
   "metadata": {},
   "source": [
    "# Bounded forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f73b521-05f2-4c53-b44c-d988bf8fe7b9",
   "metadata": {},
   "source": [
    "In forecasting, we often want to make sure the predictions stay within a certain range. For example, for predicting the sales of a product, we may require all forecasts to be positive. Thus, the forecasts may need to be bounded.\n",
    "\n",
    "With TimeGPT, you can create bounded forecasts by transforming your data prior to calling the forecast function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdf86af8-08c0-4118-bd6b-0b88a6eac3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/13_bounded_forecasts.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/tutorials/13_bounded_forecasts')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2997eb35",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we install and import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a5181e9-634c-4002-9c22-d65f0701fa14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from nixtla import NixtlaClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9872d00d-ecb1-4a14-9813-bf0c56e70392",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c7b28a",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    ">\n",
    "> To use an Azure AI endpoint, set the `base_url` argument:\n",
    ">\n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50eac17f-3c72-4f68-988a-1d5d877dff2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b99aff-aa07-44a1-b0e1-e8301f7f62bf",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77268e7",
   "metadata": {},
   "source": [
    "We use the [annual egg prices](https://github.com/robjhyndman/fpp3package/tree/master/data) dataset from [Forecasting, Principles and Practices](https://otexts.com/fpp3/). We expect egg prices to be strictly positive, so we want to bound our forecasts to be positive. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa6759e",
   "metadata": {},
   "source": [
    "::: {.callout-note}\n",
    "You can install `pyreadr` with `pip`:\n",
    "    \n",
    "```shell\n",
    "pip install pyreadr\n",
    "```\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b1e602-f8ee-49c4-8819-fcc093d64ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyreadr\n",
    "from pathlib import Path\n",
    "\n",
    "# Download and store the dataset\n",
    "url = 'https://github.com/robjhyndman/fpp3package/raw/master/data/prices.rda'\n",
    "dst_path = str(Path.cwd().joinpath('prices.rda'))\n",
    "result = pyreadr.read_r(pyreadr.download_file(url, dst_path), dst_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2c450a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>100.58</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>76.84</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>81.10</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>69.60</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>64.55</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>80.36</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>79.79</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1991-01-01</td>\n",
       "      <td>74.79</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>64.86</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>62.27</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds       y unique_id\n",
       "84 1984-01-01  100.58      eggs\n",
       "85 1985-01-01   76.84      eggs\n",
       "86 1986-01-01   81.10      eggs\n",
       "87 1987-01-01   69.60      eggs\n",
       "88 1988-01-01   64.55      eggs\n",
       "89 1989-01-01   80.36      eggs\n",
       "90 1990-01-01   79.79      eggs\n",
       "91 1991-01-01   74.79      eggs\n",
       "92 1992-01-01   64.86      eggs\n",
       "93 1993-01-01   62.27      eggs"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform some preprocessing\n",
    "df = result['prices'][['year', 'eggs']]\n",
    "df = df.dropna().reset_index(drop=True)\n",
    "df = df.rename(columns={'year':'ds', 'eggs':'y'})\n",
    "df['ds'] = pd.to_datetime(df['ds'], format='%Y')\n",
    "df['unique_id'] = 'eggs'\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301c932a",
   "metadata": {},
   "source": [
    "We can have a look at how the prices have evolved in the 20th century, demonstrating that the price is trending down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07dfce6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2932318f",
   "metadata": {},
   "source": [
    "## 3. Bounded forecasts with TimeGPT\n",
    "\n",
    "First, we transform the target data. In this case, we will log-transform the data prior to forecasting, such that we can only forecast positive prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a967678",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed = df.copy()\n",
    "df_transformed['y'] = np.log(df_transformed['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7f93bc",
   "metadata": {},
   "source": [
    "We will create forecasts for the next 10 years, and we include an 80, 90 and 99.5 percentile of our forecast distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dda23c8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: AS-JAN\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "timegpt_fcst_with_transform = nixtla_client.forecast(df=df_transformed, h=10, freq='Y', level=[80, 90, 99.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6067300",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967836fa",
   "metadata": {},
   "source": [
    "After having created the forecasts, we need to inverse the transformation that we applied earlier. With a log-transformation, this simply means we need to exponentiate the forecasts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8f6bf69",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_transform = [col for col in timegpt_fcst_with_transform if col not in ['unique_id', 'ds']]\n",
    "for col in cols_to_transform:\n",
    "    timegpt_fcst_with_transform[col] = np.exp(timegpt_fcst_with_transform[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a370f1",
   "metadata": {},
   "source": [
    "Now, we can plot the forecasts. We include a number of prediction intervals, indicating the 80, 90 and 99.5 percentile of our forecast distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67e3933e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, \n",
    "    timegpt_fcst_with_transform, \n",
    "    level=[80, 90, 99.5],\n",
    "    max_insample_length=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a587baa1",
   "metadata": {},
   "source": [
    "The forecast and the prediction intervals look reasonable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddb67e5",
   "metadata": {},
   "source": [
    "Let's compare these forecasts to the situation where we don't apply a transformation. In this case, it may be possible to forecast a negative price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c73c041",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: AS-JAN\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "timegpt_fcst_without_transform = nixtla_client.forecast(df=df, h=10, freq='Y', level=[80, 90, 99.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e8f695",
   "metadata": {},
   "source": [
    "Indeed, we now observe prediction intervals that become negative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016a37c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, \n",
    "    timegpt_fcst_without_transform, \n",
    "    level=[80, 90, 99.5],\n",
    "    max_insample_length=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66e8c60",
   "metadata": {},
   "source": [
    "For example, in 1995:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d6e583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>TimeGPT</th>\n",
       "      <th>TimeGPT-lo-99.5</th>\n",
       "      <th>TimeGPT-lo-90</th>\n",
       "      <th>TimeGPT-lo-80</th>\n",
       "      <th>TimeGPT-hi-80</th>\n",
       "      <th>TimeGPT-hi-90</th>\n",
       "      <th>TimeGPT-hi-99.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>66.859756</td>\n",
       "      <td>43.103240</td>\n",
       "      <td>46.131448</td>\n",
       "      <td>49.319034</td>\n",
       "      <td>84.400479</td>\n",
       "      <td>87.588065</td>\n",
       "      <td>90.616273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64.993477</td>\n",
       "      <td>-20.924112</td>\n",
       "      <td>-4.750041</td>\n",
       "      <td>12.275298</td>\n",
       "      <td>117.711656</td>\n",
       "      <td>134.736995</td>\n",
       "      <td>150.911066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>66.695808</td>\n",
       "      <td>6.499170</td>\n",
       "      <td>8.291150</td>\n",
       "      <td>10.177444</td>\n",
       "      <td>123.214173</td>\n",
       "      <td>125.100467</td>\n",
       "      <td>126.892446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>66.103325</td>\n",
       "      <td>17.304282</td>\n",
       "      <td>24.966939</td>\n",
       "      <td>33.032894</td>\n",
       "      <td>99.173756</td>\n",
       "      <td>107.239711</td>\n",
       "      <td>114.902368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>67.906517</td>\n",
       "      <td>4.995371</td>\n",
       "      <td>12.349648</td>\n",
       "      <td>20.090992</td>\n",
       "      <td>115.722042</td>\n",
       "      <td>123.463386</td>\n",
       "      <td>130.817663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>eggs</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>66.147575</td>\n",
       "      <td>29.162207</td>\n",
       "      <td>31.804460</td>\n",
       "      <td>34.585779</td>\n",
       "      <td>97.709372</td>\n",
       "      <td>100.490691</td>\n",
       "      <td>103.132943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>eggs</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>66.062637</td>\n",
       "      <td>14.671932</td>\n",
       "      <td>19.305822</td>\n",
       "      <td>24.183601</td>\n",
       "      <td>107.941673</td>\n",
       "      <td>112.819453</td>\n",
       "      <td>117.453343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eggs</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>68.045769</td>\n",
       "      <td>3.915282</td>\n",
       "      <td>13.188964</td>\n",
       "      <td>22.950736</td>\n",
       "      <td>113.140802</td>\n",
       "      <td>122.902573</td>\n",
       "      <td>132.176256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>eggs</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>66.718903</td>\n",
       "      <td>-42.212631</td>\n",
       "      <td>-30.583703</td>\n",
       "      <td>-18.342726</td>\n",
       "      <td>151.780531</td>\n",
       "      <td>164.021508</td>\n",
       "      <td>175.650436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>eggs</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>67.344078</td>\n",
       "      <td>-86.239911</td>\n",
       "      <td>-44.959745</td>\n",
       "      <td>-1.506939</td>\n",
       "      <td>136.195095</td>\n",
       "      <td>179.647901</td>\n",
       "      <td>220.928067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id          ds    TimeGPT  TimeGPT-lo-99.5  TimeGPT-lo-90  \\\n",
       "0      eggs  1994-01-01  66.859756        43.103240      46.131448   \n",
       "1      eggs  1995-01-01  64.993477       -20.924112      -4.750041   \n",
       "2      eggs  1996-01-01  66.695808         6.499170       8.291150   \n",
       "3      eggs  1997-01-01  66.103325        17.304282      24.966939   \n",
       "4      eggs  1998-01-01  67.906517         4.995371      12.349648   \n",
       "5      eggs  1999-01-01  66.147575        29.162207      31.804460   \n",
       "6      eggs  2000-01-01  66.062637        14.671932      19.305822   \n",
       "7      eggs  2001-01-01  68.045769         3.915282      13.188964   \n",
       "8      eggs  2002-01-01  66.718903       -42.212631     -30.583703   \n",
       "9      eggs  2003-01-01  67.344078       -86.239911     -44.959745   \n",
       "\n",
       "   TimeGPT-lo-80  TimeGPT-hi-80  TimeGPT-hi-90  TimeGPT-hi-99.5  \n",
       "0      49.319034      84.400479      87.588065        90.616273  \n",
       "1      12.275298     117.711656     134.736995       150.911066  \n",
       "2      10.177444     123.214173     125.100467       126.892446  \n",
       "3      33.032894      99.173756     107.239711       114.902368  \n",
       "4      20.090992     115.722042     123.463386       130.817663  \n",
       "5      34.585779      97.709372     100.490691       103.132943  \n",
       "6      24.183601     107.941673     112.819453       117.453343  \n",
       "7      22.950736     113.140802     122.902573       132.176256  \n",
       "8     -18.342726     151.780531     164.021508       175.650436  \n",
       "9      -1.506939     136.195095     179.647901       220.928067  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timegpt_fcst_without_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585c8db9",
   "metadata": {},
   "source": [
    "This demonstrates the value of the log-transformation to obtain bounded forecasts with TimeGPT, which allows us to obtain better calibrated prediction intervals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deae1784",
   "metadata": {},
   "source": [
    "**References**\n",
    "\n",
    "* [Hyndman, Rob J., and George Athanasopoulos (2021). \"Forecasting: Principles and Practice (3rd Ed)\"](https://otexts.com/fpp3/)\n",
    "\n",
    "\n"
   ]
  }
 ],
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